Methods for detecting diabetes indicator values

Abstract

To provide a detection marker for detecting a diabetes index, and a method for detecting a diabetes index using the detection marker.SOLUTION: A method for detecting a subject's diabetes index includes the step of measuring, about a biological sample taken from the subject, the expression level of at least one gene selected from a group of 31 specific genes or its expression product.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to a method for detecting diabetes index values using a detection marker for diabetes index values.

Background Art

[0002] Diabetes is a group of diseases characterized by chronic hyperglycemia due to insufficient insulin action and accompanied by various characteristic metabolic abnormalities. The test indices for diabetes include blood glucose levels (fasting, 2-hour oral glucose tolerance test (OGTT), random), HbA1c levels, glycated albumin levels, etc. In the clinical diagnosis of diabetes, if the fasting blood glucose level is 126 mg / dL or higher and the HbA1c level is 6.5% or higher, it is classified as diabetes type, and if the HbA1c level is less than 5.5 - 6.5%, it is said to be in the borderline region (Non-Patent Document 1).

[0003] For the prevention and treatment of diabetes, it is important to continuously manage diabetes index values such as blood glucose levels. Currently, self-measuring devices that can measure blood glucose levels at home are commercially available. However, fingerstick blood sampling is painful, causes mental distress every time it is measured, and there is a possibility of causing infectious diseases when sampling. Recently, minimally invasive blood glucose monitoring devices such as Freestyle Libre (Abbott Japan) have been developed, but they are medical devices for diabetic patients and not the type that can be used preventively by people in the pre-disease area. Therefore, a method that can non-invasively, painlessly, and easily grasp diabetes index values is desired.

[0004] In recent years, techniques for examining the current and even future physiological states in the human body by analyzing nucleic acids such as DNA and RNA in biological samples have been developed. Nucleic acids derived from living organisms can be extracted from body fluids such as blood, secretions, tissues, etc. More recently, it has been reported that RNA contained in skin surface lipids (SSL) can be used as a sample for biological analysis (Patent Document 1).

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] International Public Gazette No. 2018 / 008319 [Non-patent literature]

[0006] [Non-Patent Document 1] Diabetes (2012) 55(7): 485-504 [Overview of the project] [Problems that the invention aims to solve]

[0007] The present invention relates to a detection marker for detecting diabetes indicator values, and a method for detecting diabetes indicator values ​​using the detection marker. [Means for solving the problem]

[0008] The inventors collected SSL from subjects belonging to either the normal, borderline, or diabetic category and comprehensively analyzed the RNA expression status contained in the SSL as sequencing information. As a result, they found that the expression level of a specific gene significantly correlated with a diabetes indicator value, and that the diabetes indicator value could be detected based on the expression level of that gene.

[0009] In other words, the present invention relates to the following 1) to 3). 1) A method for detecting diabetes indicators in a subject, comprising the step of measuring the expression level of at least one gene or its expression product selected from the 31 gene groups shown in Table 1 below, in a biological sample taken from the subject. 2) A test kit for detecting diabetes indicator values ​​used in the method of 1), comprising an oligonucleotide that specifically hybridizes with the gene or nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene. 3) A detection marker for detecting diabetes indicators, comprising at least one gene or its expression product selected from the 31 gene groups shown in Table 1 below. [Effects of the Invention]

[0010] According to the present invention, it is possible to detect diabetes indicator values ​​in a simple and non-invasive manner. [Modes for carrying out the invention]

[0011] All patent, non-patent, and other publications cited herein are incorporated herein by reference in their entirety.

[0012] In this invention, the terms "nucleic acid" or "polynucleotide" mean DNA or RNA. DNA includes cDNA, genomic DNA, and synthetic DNA, and RNA includes total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.

[0013] In this invention, "gene" means a double-stranded DNA including human genomic DNA, as well as single-stranded DNA (positive strand) including cDNA, single-stranded DNA (complementary strand) having a sequence complementary to the positive strand, and fragments thereof, in which the sequence information of the bases constituting the DNA contains some kind of biological information. Furthermore, the term "gene" in this invention includes not only "genes" represented by a specific base sequence, but also their homologs (i.e., homologs or orthologs), variants such as gene polymorphisms, and derivatives. Herein, the gene names disclosed herein follow the Official Symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]).

[0014] In this invention, the term "expression product" of a gene is a concept that encompasses both the transcript and the translation product of a gene. A "transcript" is RNA produced by transcription from a gene (DNA), and a "translation product" is a protein encoded by a gene that is synthesized through translation based on RNA.

[0015] In the present invention, the "diabetes index value" refers to an examination index used for clinical diagnosis of diabetes. For example, it includes fasting blood glucose level, 2-hour value of 75g oral glucose tolerance test (OGTT), random blood glucose level, HbA1c level in blood, glycated albumin level in blood, 1,5-AG (1,5-anhydroglucitol) level in blood, and the like.

[0016] In the present invention, the "detection" of diabetes index value can also be expressed in terms such as examination, measurement, determination, or assistance in evaluation. Note that the terms "detection", "examination", "measurement", "determination", or "evaluation" of the diabetes index value product in the present invention do not include diagnosis by a doctor.

[0017] As shown in the examples described below, for subjects showing normal, borderline, or diabetic type in terms of fasting blood glucose level, HbA1c level in blood, or glycated albumin level in blood, which are known as diabetes index values, when RNA expression analysis was performed according to the following procedures 1) to 3), it was found that the 31 genes shown in Table 1 below are genes whose expression in SSL is positively correlated with any of the diabetes index values. 1) Obtain data (read count value) of the expression level of RNA derived from SSL. 2) Convert the read count value to an RPM value corrected for the difference in the total number of reads among sample subjects, and calculate the Spearman's rank correlation coefficient for the combination of the base 2 logarithm value (log2(RPM + 1) value) obtained by adding integer 1 to this and the natural logarithm value (ln(measured value + 1) value) obtained by adding integer 1 to the diabetes index value. 3) Extract and select genes with a large Spearman's rank correlation coefficient (ρ) (top genes).

[0018]

Table 1

[0019] The 31 genes shown in Table 1 above are genes that have not been reported to be related to diabetes so far.

[0020] Therefore, the gene selected from the 31 gene groups or its expression product can be a detection marker for detecting diabetes index values. Also, based on the expression level of the gene selected from the 31 gene groups or its expression product, it is possible to detect the diabetes index values of a subject. By detecting the diabetes index values, the pathological condition (disease stage) of diabetes can be grasped, which can be used for detecting the presence or absence of diabetes, detecting the risk of diabetes onset, identifying pre-diabetics, guiding and treating diabetic patients, etc.

[0021] Each of the 31 genes can be a detection marker for detecting diabetes index values alone. In the present invention, from the perspective of improving accuracy, when detecting fasting blood glucose levels as a diabetes index value, it is preferable to use a gene selected from the 13 gene groups consisting of PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2, WBP1L, SNORA10, CYTH4, NCF1B, FLJ45445, ZFP36L1, HMHA1 and FAM25B or its expression product as a detection marker for detecting fasting blood glucose levels. Among the 13 genes, preferably 2 or more, more preferably 5 or more, and still more preferably all 13 are used. Also, among the 13 genes, preferably PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2 and WBP1L, and more preferably 1 or more selected from PIM2, RPTN and EHBP1L1 are used.

[0022] Furthermore, from the viewpoint of improving accuracy, when detecting blood HbA1c levels as an indicator of diabetes, it is preferable to use a gene selected from a group of 13 genes consisting of SNORA8, PIM2, GNB2, SNORA21, SNORA38, SNORA10, REXO1L2P, SNORA71D, SNORD17, ARHGAP9, SCARNA16, SNORA16A, and SNORA14B, or its expression product, as a detection marker for detecting blood HbA1c levels. Preferably, two or more of these 13 genes are used, more preferably five or more, and even more preferably all 13. In addition, preferably one or more of these 13 genes are used, selected from SNORA8, PIM2, GNB2, SNORA21, SNORA38, and SNORA10, and more preferably from SNORA8, PIM2, and GNB2.

[0023] Furthermore, from the viewpoint of improving accuracy, when detecting blood glycated albumin levels as an indicator of diabetes, it is preferable to use a gene selected from a group of 10 genes consisting of SPRR3, SPDYE7P, SNORA38, SNORA9, SNORA81, MYO1F, KRT25, NCF1B, SNORD17, and ECE1, or its expression product, as a detection marker for detecting blood glycated albumin levels. Preferably, two or more of these 10 genes are used, more preferably five or more, and even more preferably all 10. In addition, preferably, one or more genes selected from SPRR3, SPDYE7P, SNORA38, SNORA9, and SNORA81 are used, and more preferably, one or more genes selected from SPRR3, SPDYE7P, and SNORA38 are used.

[0024] The genes that can serve as detection markers for detecting the above-mentioned diabetes indicator values ​​(hereinafter also referred to as "target genes") include genes that have a base sequence substantially identical to the base sequence of the DNA constituting the gene, insofar as they can serve as detection markers for detecting diabetes indicator values. Here, substantially identical base sequences mean, for example, that when searching using the homology calculation algorithm NCBI BLAST with the conditions expected value = 10; gap allowed; filtering = ON; match score = 1; mismatch score = -3, the base sequence of the gene constituting the gene has an identity of 90% or more, preferably 95% or more, more preferably 98% or more, and even more preferably 99% or more.

[0025] The present invention provides a method for detecting diabetes indicator values, which includes measuring the expression level of a target gene, or, in one embodiment, at least one gene selected from the 31 gene group shown in Table 1 above, or its expression product, in a biological sample taken from a subject.

[0026] The subjects in this invention include, for example, humans or non-human mammals who desire or need the detection of diabetes indicator values. For example, individuals judged to be at high risk of diabetes due to genetic factors, individuals who may be diagnosed with diabetes, specifically those who meet at least one of the following criteria: fasting blood glucose level of 126 mg / dL or higher, blood HbA1c level of 6.5% or higher, or blood glycated albumin level of 18.3% or higher; or individuals who may be judged to be at high risk of diabetes, specifically those who meet at least one of the following criteria: fasting blood glucose level of 110 to less than 126 mg / dL, blood HbA1c level of 5.6 to less than 6.5%, or blood glycated albumin level of 15.6 to less than 18.3%. The subjects are preferably humans.

[0027] The biological samples used in the present invention may be any cells, tissues, and biomaterials that can be collected non-invasively and in which the expression of the gene of the present invention is altered. Specifically, examples include body fluids such as skin, urine, saliva, sweat, stratum corneum, surface lipids (SSL), and tissue exudate, as well as feces, hair, etc. Preferably, skin or surface lipids (SSL), more preferably surface lipids (SSL).

[0028] Here, "superficial lipids (SSL)" refers to the lipid-soluble fraction present on the surface of the skin, and is sometimes called sebum. Generally, SSL mainly consists of secretions from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer covering the skin surface. SSL contains RNA expressed in skin cells. (See Patent Document 1 above). Furthermore, in the present invention, unless otherwise specified, "skin" is a general term for the region including the stratum corneum, epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands.

[0029] Any means used for the collection or removal of SSL from the skin can be employed to collect SSL from the subject's skin. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument for scraping off SSL from the skin, as described later, can be used. The SSL absorbent material or SSL adhesive material is not particularly limited as long as it is a material that has an affinity for SSL, and examples include polypropylene and pulp. More detailed examples of procedures for collecting SSL from the skin include methods of absorbing SSL onto a sheet material such as oil-blotting paper or oil-blotting film, methods of adhering SSL to a glass plate or tape, and methods of scraping off and collecting SSL with a spatula, scraper, etc. To improve the adsorption of SSL, an SSL absorbent material containing a highly lipid-soluble solvent beforehand may be used. On the other hand, since the adsorption of SSL is inhibited if the SSL absorbent material contains a highly water-soluble solvent or water, it is preferable that the content of highly water-soluble solvents or water is low. It is preferable to use the SSL absorbent material in a dry state. The skin from which SSL is collected is not particularly limited and can be any part of the body, such as the head, face, neck, trunk, hands, or feet. Areas with high sebum secretion, such as the skin of the face, are preferred.

[0030] RNA-containing SSLs collected from subjects may be stored for a certain period of time. To minimize the degradation of the contained RNA, it is preferable to store the collected SSLs under low-temperature conditions as quickly as possible after collection. The storage temperature conditions for the RNA-containing SSLs in this invention may be 0°C or lower, preferably -20±20°C to -80±20°C, more preferably -20±10°C to -80±10°C, even more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and even more preferably -20±5°C. The storage period for the RNA-containing SSLs under these low-temperature conditions is not particularly limited, but is preferably 12 months or less, for example, 6 hours to 12 months, more preferably 6 months or less, for example, 1 day to 6 months, and even more preferably 3 months or less, for example, 3 days to 3 months.

[0031] In the present invention, the objects to be measured for the expression level of a target gene or its expression product include cDNA artificially synthesized from RNA, the DNA encoding that RNA, the protein encoded by that RNA, molecules that interact with that protein, molecules that interact with that RNA, or molecules that interact with that DNA. Here, molecules that interact with RNA, DNA, or proteins include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, and their phosphorylated, alkylated, and glycosidic compounds, as well as complexes of any of the above. Furthermore, the expression level comprehensively refers to the amount of expression or activity of the gene or expression product in question.

[0032] In the method of the present invention, in a preferred embodiment, SSL is used as the biological sample. In this case, the expression level of RNA contained in the SSL is analyzed. Specifically, the RNA is converted to cDNA by reverse transcription, and then the cDNA or its amplified product is measured. For RNA extraction from SSL, methods commonly used for RNA extraction or purification from biological samples can be employed, such as the phenol / chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or methods using columns such as TRIzol®, RNeasy®, or QIAzol®, or methods using special silica-coated magnetic particles, methods using Solid Phase Reversible Immobilization magnetic particles, or extraction using commercially available RNA extraction reagents such as ISOGEN.

[0033] For the reverse transcription, primers targeting a specific RNA to be analyzed may be used, but for more comprehensive nucleic acid preservation and analysis, random primers are preferable. A general reverse transcriptase or reverse transcription reagent kit can be used for the reverse transcription. Preferably, a reverse transcriptase or reverse transcription reagent kit with high accuracy and efficiency is used, such as M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcription reagent kits, such as the PrimeScript® Reverse Transcriptase series (Takara Bio Inc.) and the SuperScript® Reverse Transcriptase series (Thermo Scientific Inc.). SuperScript® III Reverse Transcriptase and SuperScript® VILO cDNA Synthesis kit (both from Thermo Scientific Inc.) are preferably used. In the reverse transcription extension reaction, it is preferable to adjust the temperature to preferably 42°C ± 1°C, more preferably 42°C ± 0.5°C, and even more preferably 42°C ± 0.25°C, while adjusting the reaction time to preferably 60 minutes or more, more preferably 80 to 120 minutes.

[0034] Methods for measuring expression levels can be selected from nucleic acid amplification methods such as PCR, real-time RT-PCR, multiplex PCR, SmartAmp, and LAMP, which use DNA that hybridizes to RNA, cDNA, or DNA as primers; hybridization methods (DNA chips, DNA microarrays, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.) which use nucleic acids that hybridize to these as probes; methods for determining the base sequence (sequencing); or methods combining these.

[0035] In PCR, a primer pair targeting a specific DNA to be analyzed may be used to amplify only that specific DNA, or multiple primer pairs may be used to amplify multiple specific DNAs simultaneously. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method of simultaneously amplifying multiple gene regions by using multiple primer pairs simultaneously in the PCR reaction system. Multiplex PCR can be performed using commercially available kits (for example, the Ion AmpliSeqTranscriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.). The temperature for the annealing and extension reactions in the PCR cannot be generalized as it depends on the primers used, but when using the above-mentioned multiplex PCR kit, it is preferably 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even more preferably 62°C ± 0.25°C. Therefore, in the PCR, the annealing and extension reactions are preferably performed in one step. The duration of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, but is preferably 14 to 18 minutes. The conditions for the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but is preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the above temperatures and times can be performed using a thermal cycler commonly used for PCR.

[0036] The purification of the reaction product obtained by the PCR is preferably carried out by size separation of the reaction product. Size separation allows the target PCR reaction product to be separated from primers and other impurities contained in the PCR reaction mixture. DNA size separation can be carried out, for example, by a size separation column, a size separation chip, or magnetic beads that can be used for size separation. Preferred examples of magnetic beads that can be used for size separation include Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

[0037] The purified PCR reaction product may be subjected to further processing necessary for subsequent quantitative analysis. For example, the purified PCR reaction product may be prepared into a suitable buffer solution for DNA sequencing, the PCR primer region in the PCR-amplified DNA may be cleaved, or adapter sequences may be further added to the amplified DNA. For instance, the purified PCR reaction product can be prepared into a buffer solution, the amplified DNA can be subjected to removal of PCR primer sequences and adapter ligation, and the resulting reaction product can be amplified as needed to prepare a library for quantitative analysis. These operations can be performed, for example, using the 5×VILO RT Reaction Mix included with the SuperScript® VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.), the 5×Ion AmpliSeq HiFi Mix included with the Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.), and the Ion AmpliSeq Transcriptome Human Gene Expression Core Panel, according to the protocols included with each kit.

[0038] When measuring the expression level of a target gene or nucleic acid derived therefrom using Northern blot hybridization, for example, a probe DNA is first labeled with a radioisotope, a fluorescent substance, etc. Then, the resulting labeled DNA is hybridized with RNA derived from a biological sample transferred to a nylon membrane, etc., according to a conventional method. Subsequently, the double helix formed between the labeled DNA and RNA is measured by detecting the signal originating from the label.

[0039] When measuring the expression level of a target gene or nucleic acid derived therefrom using RT-PCR, for example, cDNA is first prepared from RNA derived from a biological sample according to a conventional method, and a pair of primers (a positive strand that binds to the cDNA (- strand), and a reverse strand that binds to the + strand) prepared to amplify the target gene of the present invention are hybridized with this cDNA as a template. Then, PCR is performed according to a conventional method, and the resulting amplified double-stranded DNA is detected. For the detection of the amplified double-stranded DNA, a method can be used to detect labeled double-stranded DNA produced by performing the above PCR using primers that have been previously labeled with an RI, a fluorescent substance, etc.

[0040] When measuring the expression level of a target gene or nucleic acid derived therefrom using a DNA microarray, for example, an array in which at least one nucleic acid (cDNA or DNA) derived from the target gene of the present invention is immobilized on a support is used, labeled cDNA or cRNA prepared from mRNA is bound to the microarray, and the expression level of mRNA can be measured by detecting the label on the microarray. The nucleic acid immobilized on the array can be any nucleic acid that hybridizes specifically (i.e., substantially only to the target nucleic acid) under stringent conditions. For example, it may be a nucleic acid having the entire sequence of the target gene of the present invention, or it may be a nucleic acid consisting of a partial sequence. Here, "partial sequence" refers to a nucleic acid consisting of at least 15 to 25 bases. Here, stringent conditions can typically be washing conditions of about "1×SSC, 0.1%SDS, 37°C", more stringent hybridization conditions can be about "0.5×SSC, 0.1%SDS, 42°C", and even more stringent hybridization conditions can be about "0.1×SSC, 0.1%SDS, 65°C". Hybridization conditions are described in J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition, Cold Spring Harbor Laboratory Press (2001), etc.

[0041] When measuring the expression level of a target gene or nucleic acid derived therefrom by sequencing, for example, analysis can be performed using a next-generation sequencer (e.g., the Ion S5 / XL system, Life Technologies Japan Co., Ltd.). RNA expression can be quantified based on the number of reads generated by sequencing (read count).

[0042] The probes or primers used in the above measurements, namely primers for specifically recognizing and amplifying the target gene of the present invention or nucleic acids derived therefrom, or probes for specifically detecting the RNA or nucleic acids derived therefrom, can be designed based on the base sequence constituting the target gene. Here, "specifically recognizing" means, for example, in the Northern blotting method, that substantially only the target gene of the present invention or nucleic acids derived therefrom can be detected, or for example, in the RT-PCR method, that substantially only the nucleic acids in question are amplified, so that the detected substance or product can be determined to be the gene or nucleic acids derived therefrom. Specifically, the present invention can utilize DNA consisting of a base sequence constituting the target gene, or oligonucleotides containing a certain number of nucleotides complementary to its complementary strand. Here, "complementary strand" refers to the other strand of a double-stranded DNA consisting of A:T (U in the case of RNA) and G:C base pairs. Furthermore, "complementary" is not limited to cases where the sequence is perfectly complementary in the given number of consecutive nucleotide regions, but is preferable to have 80% or more, more preferably 90% or more, and even more preferably 95% or more of identity in the base sequence. The identity of the base sequence can be determined by algorithms such as BLAST. When used as a primer, such oligonucleotides only need to be able to perform specific annealing and chain extension, and typically have a chain length of, for example, 10 bases or more, preferably 15 bases or more, more preferably 20 bases or more, and for example 100 bases or less, preferably 50 bases or less, and more preferably 35 bases or less. When used as a probe, it is sufficient that specific hybridization can be performed, and oligonucleotides that have at least a part or all of the sequence of DNA (or its complementary strand) consisting of the base sequence constituting the target gene of the present invention are used, and for example, oligonucleotides with a chain length of 10 bases or more, preferably 15 bases or more, and for example 100 bases or less, preferably 50 bases or less, and more preferably 25 bases or less are used. Here, "oligonucleotides" can be DNA or RNA, and may be synthetic or naturally occurring. Furthermore, the probes used for hybridization are usually labeled.

[0043] Furthermore, when measuring the translation product (protein) of the target gene of the present invention, molecules that interact with said protein, molecules that interact with RNA, or molecules that interact with DNA, methods such as protein chip analysis, immunoassay (e.g., ELISA), mass spectrometry (e.g., LC-MS / MS, MALDI-TOF / MS), 1-hybrid methods (PNAS 100, 12271-12276 (2003)), and 2-hybrid methods (Biol. Reprod. 58, 302-311 (1998)) can be used and can be appropriately selected depending on the target. For example, when a protein is used as the target of measurement, the method is carried out by contacting a biological sample with an antibody that specifically recognizes the expression product of the present invention, specifically an antibody that recognizes a structural characteristic site (epitope) that can distinguish the expression product protein from other proteins, detecting the polypeptide or protein in the sample bound to the antibody, and measuring its level. For example, using the Western blotting method, the above antibody is used as the primary antibody, and then the primary antibody is labeled with a radioisotope, fluorescent substance, or enzyme, and the signal derived from these labeling substances is measured with a radiation detector, fluorescence detector, etc. Furthermore, the antibodies against the above-mentioned translation products may be polyclonal or monoclonal antibodies. These antibodies can be manufactured according to known methods. Specifically, polyclonal antibodies can be obtained by using proteins expressed and purified in E. coli or other bacteria according to conventional methods, or by synthesizing partial polypeptides of such proteins according to conventional methods, immunizing non-human animals such as rabbits, and then obtaining them from the serum of the immunized animals according to conventional methods. On the other hand, monoclonal antibodies can be obtained from hybridoma cells prepared by immunizing non-human animals such as mice with proteins expressed and purified in E. coli or other bacteria according to conventional methods, or with partial polypeptides of said proteins, and then fusing the resulting spleen cells with myeloma cells. Monoclonal antibodies may also be produced using phage display (Griffiths, AD; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February 1998, pp. 102-108(7)).

[0044] Thus, the expression level of the target gene of the present invention or its expression product in a biological sample taken from a subject is measured, and the subject's diabetes indicator value is detected based on the expression level. Specifically, by performing machine learning with the expression level of the target gene or its expression product in any population having a sufficient sample size as the explanatory variable and the diabetes indicator value obtained from the population as the dependent variable, an optimal predictive model for predicting the diabetes indicator value from the expression level is constructed. Then, based on the constructed predictive model, a predicted value of the diabetes indicator value for a subject can be calculated from the expression level of the target gene or its expression product in that subject, who is the target for detecting the diabetes indicator value. When constructing a predictive model, it is preferable to use a population where the attributes of the target subjects (gender, race, age, etc.) are matched. As for expression levels, it is preferable to use the read count value, which is expression level data, the RPM value obtained by correcting the difference in the total number of reads between samples from the read count value, the value obtained by converting the RPM value to a base-2 logarithm (Log2RPM value) or the base-2 logarithm obtained by adding an integer 1 (log2(RPM+1) value), or the count value corrected using DESeq2 (Love MI et al. Genome Biol. 2014) (Normalized count value) or the base-2 logarithm obtained by adding an integer 1 (log2(Normalized count+1) value) as an indicator. Alternatively, values ​​calculated by fragments per kilobase of exon per million reads mapped (FPKM), reads per kilobase of exon per million reads mapped (RPKM), transcripts per million (TPM), etc., which are common quantitative values ​​for RNA-seq, may also be used. Furthermore, signal values ​​obtained by microarray methods and their corrected values ​​may also be used. Furthermore, when analyzing only specific target genes using RT-PCR or similar methods, it is preferable to either convert the expression level of the target gene to a relative expression level based on the expression level of housekeeping genes for analysis, or to quantify the absolute copy number (absolute quantification) using a plasmid containing the region of the target gene for analysis. The copy number obtained by digital PCR may also be used. On the other hand, as a diabetes indicator value, for example, the natural logarithm value (ln(measured value + 1) value) obtained by adding the integer 1 to the measured value can be used. Furthermore, from the viewpoint of improving accuracy, in constructing the predictive model, age information may be added as an explanatory variable in addition to the expression level of the target gene or its expression product of the present invention.

[0045] For constructing a prediction model, publicly known algorithms such as those used in machine learning can be utilized. Examples of machine learning algorithms include Random Forest, Support Vector Machine (SVM linear), Support Vector Machine (SVM rbf), Neural Network, Generalized Linear Model, Regularized Linear Discriminant Analysis, Regularized Logistic Regression, and Lasso (Least Absolute Shrinkage and Selection Operator) Regression. By inputting validation data into the constructed prediction model and calculating predicted values, the model whose predicted values ​​best match the observed values, for example, the model with the highest accuracy, can be selected as the optimal prediction model. Additionally, the recall rate, precision, and their harmonic mean (F-score) can be calculated from the predicted and observed values, and the model with the highest F-score can be selected as the optimal prediction model. Furthermore, the root mean square error (RMSE) between the predicted and actual values ​​can be used as an accuracy evaluation metric for the prediction model, and the model with the smallest RMSE can be selected as the optimal prediction model.

[0046] Furthermore, in this invention, by comparing the expression level of a target gene or its expression product with a predetermined reference value, it is possible to detect whether or not a subject has diabetes or to detect the risk of developing diabetes. Specifically, this is done by comparing the expression level of a target gene or its expression product with a predetermined reference value.

[0047] Here, the "reference value" can be predetermined based on the relationship between the diabetes indicator value and the expression level of the target gene or its expression product of the present invention. For example, a certain population can be divided into diabetic type (fasting blood glucose level: 126 mg / dL or higher, blood HbA1c level: 6.5% or higher, blood glycated albumin level: 18.3% or higher), borderline type (fasting blood glucose level: less than 110-126 mg / dL, blood HbA1c level: less than 5.6-6.5%, blood glycated albumin level: less than 15.6-18.3%) and normal type (fasting blood glucose level: less than 110 mg / dL, blood HbA1c level: less than 5.6%, blood glycated albumin level: less than 15.6%) based on diabetes indicator values, such as fasting blood glucose level, blood HbA1c level, or blood glycated albumin level. Then, values ​​determined by referring to statistical values ​​such as the mean and standard deviation of the expression levels of the target gene or its expression product in each group can be used as criteria for determining whether or not a person belongs to each group. When using multiple genes as target genes, it is preferable to determine a reference value for each gene or its expression product. The target group should preferably consist of subjects whose attributes (gender, race, age, etc.) are consistent.

[0048] There are no particular restrictions on how the reference value is determined, and it can be determined according to known methods. For example, it can be obtained from an ROC (Receiver Operating Characteristic Curve) curve created using a discriminant formula (predictive model). In an ROC curve, the vertical axis plots the probability of a positive result in a positive patient (sensitivity), and the horizontal axis plots the value obtained by subtracting the probability of a negative result in a negative patient (specificity) from 1 (false positive rate). Regarding the "true positive (sensitivity)" and "false positive (1-specificity)" shown in the ROC curve, the value (Youden index) at which "true positive (sensitivity)" - "false positive (1-specificity)" is maximized can be used as the reference value.

[0049] The diagnostic kit for detecting diabetes indicator values ​​of the present invention contains diagnostic reagents for measuring the expression level of the target gene or its expression product in a biological sample isolated from a patient. Specifically, these include reagents for nucleic acid amplification and hybridization, including oligonucleotides (e.g., primers for PCR) that specifically bind (hybridize) to the target gene or nucleic acid derived therefrom, or reagents for immunological measurement, including antibodies that recognize the expression product (protein) of the target gene of the present invention. The oligonucleotides, antibodies, etc., included in the kit can be obtained by known methods as described above. Furthermore, the test kit may include, in addition to the antibodies and nucleic acids mentioned above, labeling reagents, buffer solutions, chromogenic substrates, secondary antibodies, blocking agents, equipment necessary for the test, control reagents used as positive and negative controls, and tools for collecting biological samples (for example, oil-absorbing film for collecting SSL).

[0050] With regard to the embodiments described above, the present invention further discloses the following aspects.

[0051] <1> A method for detecting diabetes indicator values ​​in a subject, comprising the step of measuring the expression level of at least one gene or its expression product selected from the 31 gene groups shown in Table 1 above, in a biological sample taken from the subject.

[0052] <2> The aforementioned gene is selected from a group of 13 genes consisting of PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2, WBP1L, SNORA10, CYTH4, NCF1B, FLJ45445, ZFP36L1, HMHA1, and FAM25B, and the aforementioned diabetes indicator value is the fasting blood glucose level. <1> The detection method described. <3> Of the 13 types mentioned above, preferably two or more, more preferably five or more, and even more preferably all 13 genes or their expression products are measured. <2> The detection method described. <4> Of the 13 types mentioned above, the expression level of one or more genes or their expression products, preferably selected from PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2, and WBP1L, and more preferably from PIM2, RPTN, and EHBP1L1, is measured. <2> The detection method described. <5> The aforementioned gene is selected from a group of 13 genes consisting of SNORA8, PIM2, GNB2, SNORA21, SNORA38, SNORA10, REXO1L2P, SNORA71D, SNORD17, ARHGAP9, SCARNA16, SNORA16A, and SNORA14B, and the aforementioned diabetes indicator value is the blood HbA1c value. <1> The detection method described. <6> Of the 13 types mentioned above, preferably two or more, more preferably five or more, and even more preferably all 13 genes or their expression products are measured. <5> The detection method described. <7> Of the 13 types mentioned above, the expression level of one or more genes or their expression products selected from SNORA8, PIM2, GNB2, SNORA21, SNORA38, and SNORA10, and more preferably SNORA8, PIM2, and GNB2, is measured. <5> The detection method described. <8> The aforementioned gene is selected from a group of 10 genes consisting of SPRR3, SPDYE7P, SNORA38, SNORA9, SNORA81, MYO1F, KRT25, NCF1B, SNORD17, and ECE1, and the aforementioned diabetes indicator value is the serum glycated albumin level. <1> The detection method described. <9> Of the 10 types mentioned above, preferably two or more, more preferably five or more, and even more preferably all 10 genes or their expression products are measured. <8> The detection method described. <10> Of the 10 types mentioned above, the expression level of one or more genes or their expression products, preferably selected from SPRR3, SPDYE7P, SNORA38, SNORA9, and SNORA81, and more preferably from SPRR3, SPDYE7P, and SNORA38, is measured. <8> The detection method described. <11> The target for measuring the expression level of the gene or its expression product is preferably a cDNA artificially synthesized from RNA, the DNA encoding that RNA, the protein encoded by that RNA, a molecule that interacts with that protein, a molecule that interacts with that RNA, or a molecule that interacts with that DNA. <1> ~ <10> A detection method described in any of the following. <12> The expression level of the gene or its expression product is preferably the mRNA expression level. <1> ~ <11> A detection method described in any of the following. <13> The biological sample is preferably a subject's body fluid such as skin, urine, saliva, sweat, stratum corneum, surface lipids (SSL), tissue exudate, feces, or hair, more preferably skin or surface lipids (SSL), and even more preferably surface lipids (SSL). <1> ~ <12> A detection method described in any of the following. <14> This includes detecting the subject's diabetes indicator value based on the expression level of the gene or its expression product. <1> ~ <13> A detection method described in any of the following. <15> Preferably, this includes detecting diabetes indicator values ​​using a predictive model based on the expression level of the gene or its expression product, The predictive model is constructed with the measured expression level of the gene or its expression product, or the measured expression level and the subject's age, as explanatory variables, and the diabetes index value as the dependent variable. <1> ~ <14> A detection method described in any of the following. <16> The system contains an oligonucleotide that specifically hybridizes with the gene or nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene. <1> ~ <15> A test kit for detecting diabetes indicator values ​​used in any of the detection methods described above. <17> A detection marker for detecting diabetes indicators, comprising at least one gene or its expression product selected from the 31 gene groups shown in Table 1. <18> The aforementioned gene is at least one gene selected from a group of 13 genes consisting of PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2, WBP1L, SNORA10, CYTH4, NCF1B, FLJ45445, ZFP36L1, HMHA1, and FAM25B, or its expression product, and the diabetes indicator value is fasting blood glucose level. <17> The detection marker described. <19> The aforementioned gene is at least one gene selected from a group of 13 genes consisting of SNORA8, PIM2, GNB2, SNORA21, SNORA38, SNORA10, REXO1L2P, SNORA71D, SNORD17, ARHGAP9, SCARNA16, SNORA16A, and SNORA14B, or its expression product, and the diabetes indicator value is the blood HbA1c value. <17> The detection marker described. <20> The aforementioned gene is at least one gene selected from a group of 10 genes consisting of SPRR3, SPDYE7P, SNORA38, SNORA9, SNORA81, MYO1F, KRT25, NCF1B, SNORD17, and ECE1, or its expression product, and the diabetes indicator value is the serum glycated albumin level. <17> The detection marker described. [Examples]

[0053] Example 1: Detection of diabetes indicator values ​​using RNA extracted from SSL (1) 1) Measurement of subjects and fasting blood glucose levels The study involved 376 women aged 20 to 80 years. Participants were prohibited from eating breakfast on the day of their visit to the study site. Upon arrival, blood samples were collected by nurses under the supervision of a physician, following standard procedures. Fasting blood glucose levels were measured using these blood samples. The results showed that the mean ± standard deviation of fasting blood glucose levels in this group was 93.7 ± 15.4 mg / dL, and the median was 91 mg / dL.

[0054] 2) SSL collection Sebum was collected from the entire face of each subject using an oil-absorbing film (5 x 8 cm, made of polypropylene, 3M). The oil-absorbing film was then transferred to a vial and stored at -80°C until used for RNA extraction.

[0055] 3) RNA preparation and sequencing The oil-absorbing film described in 2) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the included protocol. Based on the extracted RNA, cDNA was synthesized by reverse transcription at 42°C for 90 minutes using the SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.). Random primers included in the kit were used as primers for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR. Multiplex PCR was performed using the Ion AmpliSeqTranscriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) under the conditions [99°C, 2 min → (99°C, 15 sec → 62°C, 16 min) × 20 cycles → 4°C, Hold]. The obtained PCR products were purified with Ampure XP (Beckman Coulter, Inc.), and then the buffer was reconstituted, primer sequences were digested, adapter ligation and purification were performed, and amplification was carried out to prepare the library. The prepared library was loaded onto an Ion 540 Chip and sequenced using the Ion S5 / XL system (Life Technologies Japan Co., Ltd.).

[0056] 4) Data Analysis (1) Data used The expression levels (read counts) of SSL-derived RNA measured in step 3) above were obtained and converted to RPM values ​​corrected for differences in the total number of reads among the sample subjects. To construct the machine learning model, the base-2 logarithm (log2(RPM+1) value) obtained by adding an integer 1 to the RPM value was used to approximate the RPM values, which follow a negative binomial distribution, to a normal distribution. Of the expression level data from all sample subjects, only 2227 genes for which expression level data without missing values ​​was obtained for more than 90% of the sample subjects were used for the following analysis. Furthermore, to approximate fasting blood glucose levels to a normal distribution, the natural logarithm (ln(measured value + 1) value) obtained by adding the integer 1 to the above measured value was used.

[0057] (2) Dataset splitting Of the 376 subjects in the dataset, RNA profile data from 304 subjects were used as training data for model construction, and RNA profile data from the remaining 72 subjects were used as test data for evaluating model accuracy.

[0058] (3) Selection of characteristic genes Spearman's rank correlation coefficient was calculated for all combinations of fasting blood glucose levels (ln(measured value + 1)) and gene RNA expression data (log2(RPM+1)) for 304 individuals. As a result, we extracted genes with large Spearman rank correlation coefficients (ρ) and selected 13 genes, as shown in Table 2. None of these 13 genes have been previously reported to be associated with fasting blood glucose levels.

[0059] [Table 2]

[0060] (4) Model construction (a) Prediction using feature genes A predictive model was constructed using the expression levels (log2(RPM+1) values) of the feature genes selected from SSL-derived RNA, which served as the training data, as explanatory variables, and fasting blood glucose levels (ln(measured value+1) values) as the dependent variable. The predictive model was trained using six algorithms: a generalized linear model, a neural network, Lasso regression, a random forest, a linear kernel support vector machine (SVM linear), and an rbf kernel support vector machine (SVM rbf), with 10-fold cross-validation. For each algorithm, the training data was input into the trained model, the root mean square error (RMSE) was calculated, and the model with the smallest RMSE was adopted. The selected model was used to input the feature gene expression levels (log2(RPM+1) values) from the test data, and the predicted value of fasting blood glucose level (ln(measured value+1) value) was calculated. Finally, Spearman's rank correlation coefficient was calculated from the predicted value and the measured value.

[0061] (b) Prediction based on feature genes + age A predictive model was constructed using the expression levels (log2(RPM+1) values) of the feature genes selected from SSL-derived RNA, which constitute the training data, and the subject's age as explanatory variables, with fasting blood glucose levels (ln(measured value+1) value) as the dependent variable. Subsequently, the predictive model was constructed using the same method as in (a) above, and finally, Spearman's rank correlation coefficient was calculated from the predicted values ​​and measured values.

[0062] 5) Results In a prediction model (random forest) using 13 feature genes, the rank correlation coefficient on the test data was 0.418 (p<0.001), demonstrating the ability to predict fasting blood glucose levels. In a prediction model (Lasso regression) using 13 feature genes and age, the rank correlation coefficient was 0.607 (p<0.001), showing an improvement in prediction accuracy.

[0063] Example 2: Detection of diabetes indicator values ​​using RNA extracted from SSL (2) 1) Measurement of subjects and blood HbA1c levels The subjects were the same as in Example 1, and blood HbA1c levels were measured using the blood collected in Example 1. As a result, the mean ± standard deviation of blood HbA1c levels in this group was 5.7 ± 0.5%, and the median was 5.6%.

[0064] 2) SSL collection, 3) RNA preparation and sequencing The procedure was carried out in the same manner as in Example 1.

[0065] 4) Data Analysis (1) Data used The expression levels (read counts) of SSL-derived RNA measured in step 3) above were obtained and converted to RPM values ​​corrected for differences in the total number of reads among the sample subjects. To construct the machine learning model, the base-2 logarithm (log2(RPM+1) value) obtained by adding an integer 1 to the RPM value was used to approximate the RPM values, which follow a negative binomial distribution, to a normal distribution. Of the expression level data from all sample subjects, only 2227 genes for which expression level data without missing values ​​was obtained for more than 90% of the sample subjects were used for the following analysis. Furthermore, to approximate the blood HbA1c values ​​to a normal distribution, the natural logarithm (ln(measured value + 1) value) obtained by adding the integer 1 to the measured value was used.

[0066] (2) Dataset splitting Of the 376 subjects in the dataset, RNA profile data from 304 subjects were used as training data for model construction, and RNA profile data from the remaining 72 subjects were used as test data for evaluating model accuracy.

[0067] (3) Selection of characteristic genes Spearman's rank correlation coefficient was calculated for combinations of blood HbA1c values ​​(ln(measured value + 1)) and all gene RNA expression data (log2(RPM+1) values) from 304 individuals. As a result, we extracted genes with large Spearman rank correlation coefficients (ρ) and selected 13 genes, as shown in Table 3. None of these 13 genes have been previously reported to be associated with blood HbA1c levels.

[0068] [Table 3]

[0069] (4) Model construction (a) Prediction using feature genes A predictive model was constructed using the expression levels (log2(RPM+1) values) of the feature genes selected from SSL-derived RNA, which served as the training data, as explanatory variables, and the blood HbA1c value (ln(measured value+1) value) as the dependent variable. The predictive model was trained using six algorithms: a generalized linear model, a neural network, Lasso regression, a random forest, a linear kernel support vector machine (SVM linear), and an rbf kernel support vector machine (SVM rbf), with 10-fold cross-validation. For each algorithm, the training data was input into the trained model, the root mean square error (RMSE) was calculated, and the model with the smallest RMSE was adopted. The selected model was used to input the feature gene expression levels (log2(RPM+1) values) from the test data, and the predicted value of the blood HbA1c level (ln(measured value+1) value) was calculated. Finally, Spearman's rank correlation coefficient was calculated from the predicted and measured values.

[0070] (b) Prediction based on feature genes + age A predictive model was constructed using the expression levels (log2(RPM+1) values) of the feature genes selected from SSL-derived RNA, which constitute the training data, and the subject's age as explanatory variables, and the blood HbA1c value (ln(measured value+1) value) as the dependent variable. Subsequently, the predictive model was constructed using the same method as in (a) above, and finally, Spearman's rank correlation coefficient was calculated from the predicted values ​​and the measured values.

[0071] 5) Results In a prediction model using 13 feature genes (rbf kernel support vector machine), the rank correlation coefficient on the test data was 0.267 (p<0.05), demonstrating the ability to predict blood HbA1c levels. In the case of a prediction model using 13 feature genes and age (Lasso regression), the rank correlation coefficient was 0.45 (p<0.001), showing an improvement in prediction accuracy.

[0072] Example 3: Detection of diabetes indicator values ​​using RNA extracted from SSL (3) 1) Measurement of subjects and blood glycoalbumin levels The subjects were the same as in Example 1, and blood collected in Example 1 was used to measure serum glycated albumin levels. As a result, the mean ± standard deviation of serum glycated albumin levels in this group was 14.6 ± 1.7%, and the median was 14.5%.

[0073] 2) SSL collection, 3) RNA preparation and sequencing The procedure was carried out in the same manner as in Example 1.

[0074] 4) Data Analysis (1) Data used The expression levels (read counts) of SSL-derived RNA measured in step 3) above were obtained and converted to RPM values ​​corrected for differences in the total number of reads among the sample subjects. To construct the machine learning model, the base-2 logarithm (log2(RPM+1) value) obtained by adding an integer 1 to the RPM value was used to approximate the RPM values, which follow a negative binomial distribution, to a normal distribution. Of the expression level data from all sample subjects, only 2227 genes for which expression level data without missing values ​​was obtained for more than 90% of the sample subjects were used for the following analysis. Furthermore, to approximate the blood glycated albumin levels to a normal distribution, the natural logarithm (ln(measured value + 1) value) obtained by adding the integer 1 to the measured value was used.

[0075] (2) Dataset splitting Of the 376 subjects in the dataset, RNA profile data from 304 subjects were used as training data for model construction, and RNA profile data from the remaining 72 subjects were used as test data for evaluating model accuracy.

[0076] (3) Selection of characteristic genes Spearman's rank correlation coefficient was calculated for combinations of blood glycated albumin levels (ln(measured value + 1)) and all gene RNA expression data (log2(RPM+1) values) from 304 individuals. As a result, we extracted genes with large Spearman rank correlation coefficients (ρ) and selected the 10 genes shown in Table 4. None of these 10 genes have been previously reported to be associated with serum glycated albumin levels.

[0077] [Table 4]

[0078] (4) Model construction (a) Prediction using feature genes A predictive model was constructed using the expression levels (log2(RPM+1) values) of the feature genes selected from SSL-derived RNA, which served as the training data, as explanatory variables, and the serum glycated albumin level (ln(measured value+1) value) as the dependent variable. The predictive model was trained using six algorithms: a generalized linear model, a neural network, Lasso regression, a random forest, a linear kernel support vector machine (SVM linear), and an rbf kernel support vector machine (SVM rbf), with 10-fold cross-validation. For each algorithm, the training data was input into the trained model, the root mean square error (RMSE) was calculated, and the model with the smallest RMSE was adopted. The selected model was used to input the feature gene expression levels (log2(RPM+1) values) from the test data, and the predicted values ​​of serum glycated albumin levels (ln(measured value+1) values) were calculated. Finally, Spearman's rank correlation coefficient was calculated from the predicted and measured values.

[0079] 5) Results In a predictive model (a linear kernel support vector machine) using 10 feature genes, the rank correlation coefficient on the test data was 0.345 (p<0.01), demonstrating that it is possible to predict blood glycated albumin levels.

Claims

1. A method for detecting a subject's diabetes indicator, comprising measuring the expression levels of all genes or their expression products from a group of 13 genes consisting of PIM2, RPTN, EHBP1L1, CTDSP2, EFHD2, WBP1L, SNORA10, CYTH4, NCF1B, FLJ45445, ZFP36L1, HMHA1, and FAM25B in skin surface lipids collected from a subject, and detecting the subject's diabetes indicator value based on the expression levels of said genes or their expression products, wherein This includes detecting diabetes indicator values ​​using a predictive model based on the expression level of the gene or its expression product, The aforementioned predictive model is a predictive model constructed by machine learning using pre-trained data, with the measured expression level of the gene or its expression product and the subject's age as explanatory variables, and the diabetes indicator value as the dependent variable. A detection method in which the aforementioned diabetes indicator value is the fasting blood glucose level.

2. A method for detecting a subject's diabetes indicator, comprising measuring the expression levels of all genes or their expression products from a group of 13 genes consisting of SNORA8, PIM2, GNB2, SNORA21, SNORA38, SNORA10, REXO1L2P, SNORA71D, SNORD17, ARHGAP9, SCARNA16, SNORA16A, and SNORA14B in skin surface lipids collected from the subject, and detecting the subject's diabetes indicator value based on the expression levels of said genes or their expression products, wherein This includes detecting diabetes indicator values ​​using a predictive model based on the expression level of the gene or its expression product, The aforementioned predictive model is a predictive model constructed by machine learning using pre-trained data, with the measured expression level of the gene or its expression product and the subject's age as explanatory variables, and the diabetes indicator value as the dependent variable. A detection method wherein the aforementioned diabetes indicator value is the blood HbA1c value.

3. The detection method according to claim 1 or 2, wherein the expression level of the gene or its expression product is the expression level of mRNA.

4. A test kit for detecting diabetes indicator values ​​used in the detection method according to any one of claims 1 to 3, comprising an oligonucleotide that specifically hybridizes with the gene or nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene.

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